#tidytuesday 2019 week 52: Wolf presence in France
Olivier Gimenez
January 2, 2020
1 Read in data
From the official website http://carmen.carmencarto.fr/38/Loup.map#, just click on icon 
on top and tick everything in section ‘Répartition par maille’. Then read in all shapefiles from a ‘data’ repository.
## [1] "data/wolf_presence/loup_1995_maille_L93.shp"
## [2] "data/wolf_presence/loup_1996_maille_L93.shp"
## [3] "data/wolf_presence/loup_1997_maille_L93.shp"
## [4] "data/wolf_presence/loup_1998_maille_L93.shp"
## [5] "data/wolf_presence/loup_1999_maille_L93.shp"
## [6] "data/wolf_presence/loup_2000_maille_L93.shp"
## [7] "data/wolf_presence/loup_2001_maille_L93.shp"
## [8] "data/wolf_presence/loup_2002_maille_L93.shp"
## [9] "data/wolf_presence/loup_2003_maille_L93.shp"
## [10] "data/wolf_presence/loup_2004_maille_L93.shp"
## [11] "data/wolf_presence/loup_2005_maille_L93.shp"
## [12] "data/wolf_presence/loup_2006_maille_L93.shp"
## [13] "data/wolf_presence/loup_2007_maille_L93.shp"
## [14] "data/wolf_presence/loup_2008_maille_L93.shp"
## [15] "data/wolf_presence/loup_2009_maille_L93.shp"
## [16] "data/wolf_presence/loup_2010_maille_L93.shp"
## [17] "data/wolf_presence/loup_2011_maille_L93.shp"
## [18] "data/wolf_presence/loup_2012_maille_L93.shp"
## [19] "data/wolf_presence/loup_2013_maille_L93.shp"
## [20] "data/wolf_presence/loup_2014_maille_L93.shp"
## [21] "data/wolf_presence/loup_2015_maille_L93.shp"
## [22] "data/wolf_presence/loup_2016_maille_L93.shp"
## [23] "data/wolf_presence/loup_2017_maille_L93.shp"
## [24] "data/wolf_presence/loup_2018_maille_L93.shp"
## [25] "data/wolf_presence/loup_2019_maille_L93.shp"## Reading layer `loup_1995_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_1995_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 14 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 919631 ymin: 6289118 xmax: 1070962 ymax: 6788243
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_1996_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_1996_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 18 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 919631 ymin: 6289118 xmax: 1080016 ymax: 6788243
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_1997_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_1997_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 30 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 919631 ymin: 6290028 xmax: 1080900 ymax: 6788243
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_1998_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_1998_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 51 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 679052.9 ymin: 6290028 xmax: 1080900 ymax: 6484095
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_1999_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_1999_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 94 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 633149.3 ymin: 6167772 xmax: 1080900 ymax: 6504026
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_2000_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_2000_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 119 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 633149.3 ymin: 6167772 xmax: 1080900 ymax: 6602731
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_2001_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_2001_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 131 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 633149.3 ymin: 6167772 xmax: 1080900 ymax: 6602731
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_2002_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_2002_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 128 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 633149.3 ymin: 6167772 xmax: 1080900 ymax: 6602731
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_2003_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_2003_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 151 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 594200.7 ymin: 6154019 xmax: 1080900 ymax: 6573762
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_2004_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_2004_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 180 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 594200.7 ymin: 6154019 xmax: 1080900 ymax: 6573762
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_2005_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_2005_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 212 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 594200.7 ymin: 6136847 xmax: 1080900 ymax: 6573762
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_2006_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_2006_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 236 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 603291.5 ymin: 6136847 xmax: 1080900 ymax: 6571942
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_2007_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_2007_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 260 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 603291.5 ymin: 6136847 xmax: 1080900 ymax: 6593682
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_2008_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_2008_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 271 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 594200.7 ymin: 6138722 xmax: 1080900 ymax: 6617164
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_2009_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_2009_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 280 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 594200.7 ymin: 6138722 xmax: 1080900 ymax: 6617164
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_2010_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_2010_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 302 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 594200.7 ymin: 6138722 xmax: 1080900 ymax: 6617164
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_2011_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_2011_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 334 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 594200.7 ymin: 6138722 xmax: 1080900 ymax: 6785601
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_2012_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_2012_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 371 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 569971.8 ymin: 6135908 xmax: 1080900 ymax: 6814421
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_2013_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_2013_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 396 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 474304.3 ymin: 6135908 xmax: 1080900 ymax: 6833406
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_2014_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_2014_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 449 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 474304.3 ymin: 6135908 xmax: 1080900 ymax: 6881530
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_2015_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_2015_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 507 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 474304.3 ymin: 6135908 xmax: 1080900 ymax: 6941296
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_2016_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_2016_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 555 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 498216.7 ymin: 6135908 xmax: 1080900 ymax: 6941296
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_2017_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_2017_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 569 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 498216.7 ymin: 6135908 xmax: 1080900 ymax: 6941296
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_2018_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_2018_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 608 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 498216.7 ymin: 6135908 xmax: 1080900 ymax: 7010784
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## Reading layer `loup_2019_maille_L93' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/wolf_presence/loup_2019_maille_L93.shp' using driver `ESRI Shapefile'
## Simple feature collection with 644 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 409798 ymin: 6135908 xmax: 1080900 ymax: 7010784
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defsHave a look to the structure:
## Observations: 6,910
## Variables: 10
## $ ID <int> 268171, 269499, 269548, 272856, 273524, 274193, 274194, 2748…
## $ CD_SIG <fct> 10kmE400N241, 10kmE402N229, 10kmE402N278, 10kmE407N236, 10km…
## $ PRESENCE <fct> Occasionnelle, Occasionnelle, Occasionnelle, Occasionnelle, …
## $ ANNEE <int> 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1995, …
## $ DATE_MIN <fct> 01/04/1992, 01/04/1992, 01/04/1992, 01/04/1992, 01/04/1992, …
## $ DATE_MAX <fct> 31/03/1995, 31/03/1995, 31/03/1995, 31/03/1995, 31/03/1995, …
## $ X <dbl> 938325.1, 968812.9, 925059.1, 1012321.7, 1024029.4, 1034852.…
## $ Y <dbl> 6412471, 6294565, 6782805, 6368972, 6349922, 6340846, 635082…
## $ ESPECE <fct> Loup gris, Loup gris, Loup gris, Loup gris, Loup gris, Loup …
## $ geometry <POLYGON [m]> POLYGON ((943737.8 6407943,..., POLYGON ((974223.6 6…Have a look to the data:
We will also need a map of France:
## Reading layer `france_union_departements' from data source `/Users/oliviergimenez/Desktop/tidytuesday/2019/week52/data/map_france/france_union_departements.shp' using driver `ESRI Shapefile'
## Simple feature collection with 1 feature and 12 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 99217.1 ymin: 6049646 xmax: 1242417 ymax: 7110480
## epsg (SRID): 2154
## proj4string: +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs2 Data wrangling
presence %>%
as_data_frame() %>%
group_by(ANNEE, PRESENCE) %>%
summarise(count = n()) %>%
ggplot() +
aes(x = ANNEE, y = count, color = PRESENCE) +
geom_line() +
scale_color_manual(values = c('steelblue1','steelblue4'),
name = "Presence is",
labels = c("occasional", "regular")) +
labs(x = 'year',
y = 'number of cells where the species was detected',
title = "Trend in gray wolf (Canis lupus) presence in France",
subtitle = 'Data: French Game and Wildlife Agency (http://carmen.carmencarto.fr/38/Loup.map#)',
caption = 'Visualisation by Olivier Gimenez for #tidytuesday 2019 week 52 \n Code: https://bit.ly/2QgfhhD') 
3 Visualize
3.1 Static map
presence %>%
ggplot() +
geom_sf(data = map, colour = "black", fill = "white", lwd = 0.2) +
geom_sf(data = presence, aes(fill = PRESENCE), lwd = 0) +
scale_fill_manual(values = c('steelblue1','steelblue4'),
name = "Presence is",
labels = c("occasional", "regular")) +
facet_wrap(. ~ ANNEE) + # yearly maps prior animating
labs(title = "Gray wolf (Canis lupus) presence in France",
# labs(title = emo::ji_glue(":wolf: presence in :france:"), # tried to have emojis in the title, did not work
subtitle = 'Data: French Game and Wildlife Agency (http://carmen.carmencarto.fr/38/Loup.map#)',
caption = 'Visualisation by Olivier Gimenez for #tidytuesday 2019 week 52 \n Code: https://bit.ly/2QgfhhD') 
3.2 Dynamic map
anim_map_wolf <- presence %>%
ggplot() +
geom_sf(data = map, colour = "black", fill = "white", lwd = 0.2) +
geom_sf(data = presence, aes(fill = PRESENCE), lwd = 0) +
scale_fill_manual(values = c('steelblue1','steelblue4'),
name = "Presence is",
labels = c("occasional", "regular")) +
labs(title = "Gray wolf (Canis lupus) presence in France, year {frame_time}", # title with dynamic year
# labs(title = emo::ji_glue(":wolf: presence in :france:"), # tried to have emojis in the title, did not work
subtitle = 'Data: French Game and Wildlife Agency (http://carmen.carmencarto.fr/38/Loup.map#)',
caption = 'Visualisation by Olivier Gimenez for #tidytuesday 2019 week 52 \n Code: https://bit.ly/2QgfhhD') +
#scale bar
annotation_scale(location = "bl", #bottom left
height = unit(0.2, "cm"), #how thick it should be
width_hint = 0.1, #how long relative to the data
text_cex = 0.8) + #size of the text
#compass arrow
annotation_north_arrow(location = "tr", #top right
which_north = "grid", #grid or true, depending on data projection
height = unit(1, "cm"), #how tall the arrow should be
width = unit(0.5, "cm"), #how wide the arrow should be
#below: style of the arrow
style = north_arrow_orienteering(fill = c("black", "black"),
text_size = 9)) +
# animated maps https://www.aliesdataspace.com/2019/05/animation-station/
transition_time(ANNEE)
animate(anim_map_wolf, height = 600, width = 600)
4 Session info
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.1 (2019-07-05)
## os macOS Sierra 10.12.6
## system x86_64, darwin15.6.0
## ui X11
## language (EN)
## collate fr_FR.UTF-8
## ctype fr_FR.UTF-8
## tz Europe/Paris
## date 2020-01-02
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib source
## assertthat 0.2.1 2019-03-21 [2] CRAN (R 3.6.0)
## backports 1.1.5 2019-10-02 [2] CRAN (R 3.6.0)
## broom 0.5.3 2019-12-14 [2] CRAN (R 3.6.0)
## callr 3.4.0 2019-12-09 [2] CRAN (R 3.6.0)
## cellranger 1.1.0 2016-07-27 [2] CRAN (R 3.6.0)
## class 7.3-15 2019-01-01 [2] CRAN (R 3.6.1)
## classInt 0.4-2 2019-10-17 [2] CRAN (R 3.6.0)
## cli 2.0.0 2019-12-09 [2] CRAN (R 3.6.0)
## cluster 2.1.0 2019-06-19 [2] CRAN (R 3.6.1)
## codetools 0.2-16 2018-12-24 [2] CRAN (R 3.6.1)
## colorspace 1.4-1 2019-03-18 [2] CRAN (R 3.6.0)
## crayon 1.3.4 2017-09-16 [2] CRAN (R 3.6.0)
## DBI 1.1.0 2019-12-15 [2] CRAN (R 3.6.0)
## dbplyr 1.4.2 2019-06-17 [2] CRAN (R 3.6.0)
## desc 1.2.0 2018-05-01 [2] CRAN (R 3.6.0)
## devtools 2.2.1 2019-09-24 [2] CRAN (R 3.6.0)
## digest 0.6.23 2019-11-23 [2] CRAN (R 3.6.1)
## dplyr * 0.8.3 2019-07-04 [2] CRAN (R 3.6.0)
## e1071 1.7-3 2019-11-26 [2] CRAN (R 3.6.0)
## ellipsis 0.3.0 2019-09-20 [2] CRAN (R 3.6.0)
## evaluate 0.14 2019-05-28 [2] CRAN (R 3.6.0)
## extrafont * 0.17 2014-12-08 [2] CRAN (R 3.6.0)
## extrafontdb 1.0 2012-06-11 [2] CRAN (R 3.6.0)
## fansi 0.4.0 2018-10-05 [2] CRAN (R 3.6.0)
## farver 2.0.1 2019-11-13 [2] CRAN (R 3.6.0)
## forcats * 0.4.0 2019-02-17 [2] CRAN (R 3.6.0)
## fs 1.3.1 2019-05-06 [2] CRAN (R 3.6.0)
## generics 0.0.2 2018-11-29 [2] CRAN (R 3.6.0)
## gganimate * 1.0.4 2019-11-18 [2] CRAN (R 3.6.0)
## ggplot2 * 3.2.1 2019-08-10 [2] CRAN (R 3.6.0)
## ggrepel * 0.8.1 2019-05-07 [2] CRAN (R 3.6.0)
## ggspatial * 1.0.3 2018-12-14 [1] CRAN (R 3.6.0)
## gifski 0.8.6 2018-09-28 [2] CRAN (R 3.6.0)
## glue 1.3.1.9000 2019-11-24 [2] Github (tidyverse/glue@c02d7d4)
## gridExtra 2.3 2017-09-09 [2] CRAN (R 3.6.0)
## gtable 0.3.0 2019-03-25 [2] CRAN (R 3.6.0)
## haven 2.2.0 2019-11-08 [2] CRAN (R 3.6.0)
## hms 0.5.2 2019-10-30 [2] CRAN (R 3.6.0)
## htmltools 0.4.0 2019-10-04 [2] CRAN (R 3.6.0)
## httr 1.4.1 2019-08-05 [2] CRAN (R 3.6.1)
## jsonlite 1.6 2018-12-07 [2] CRAN (R 3.6.0)
## KernSmooth 2.23-16 2019-10-15 [2] CRAN (R 3.6.0)
## knitr 1.26 2019-11-12 [2] CRAN (R 3.6.0)
## lattice * 0.20-38 2018-11-04 [2] CRAN (R 3.6.1)
## lazyeval 0.2.2 2019-03-15 [2] CRAN (R 3.6.0)
## lifecycle 0.1.0 2019-08-01 [2] CRAN (R 3.6.0)
## lpSolve 5.6.13.3 2019-08-19 [2] CRAN (R 3.6.0)
## lubridate * 1.7.4 2018-04-11 [2] CRAN (R 3.6.0)
## magrittr 1.5 2014-11-22 [2] CRAN (R 3.6.0)
## MASS 7.3-51.5 2019-12-20 [2] CRAN (R 3.6.0)
## Matrix 1.2-18 2019-11-27 [2] CRAN (R 3.6.0)
## memoise 1.1.0 2017-04-21 [2] CRAN (R 3.6.0)
## mgcv 1.8-31 2019-11-09 [2] CRAN (R 3.6.0)
## modelr 0.1.5 2019-08-08 [2] CRAN (R 3.6.0)
## munsell 0.5.0 2018-06-12 [2] CRAN (R 3.6.0)
## nlme 3.1-143 2019-12-10 [2] CRAN (R 3.6.0)
## permute * 0.9-5 2019-03-12 [2] CRAN (R 3.6.0)
## pillar 1.4.2 2019-06-29 [2] CRAN (R 3.6.0)
## pkgbuild 1.0.6 2019-10-09 [2] CRAN (R 3.6.0)
## pkgconfig 2.0.3 2019-09-22 [2] CRAN (R 3.6.0)
## pkgload 1.0.2 2018-10-29 [2] CRAN (R 3.6.0)
## plyr 1.8.5 2019-12-10 [2] CRAN (R 3.6.0)
## png 0.1-7 2013-12-03 [2] CRAN (R 3.6.0)
## prettyunits 1.0.2 2015-07-13 [2] CRAN (R 3.6.0)
## processx 3.4.1 2019-07-18 [2] CRAN (R 3.6.0)
## progress 1.2.2 2019-05-16 [2] CRAN (R 3.6.0)
## ps 1.3.0 2018-12-21 [2] CRAN (R 3.6.0)
## purrr * 0.3.3.9000 2019-11-24 [2] Github (tidyverse/purrr@0168a59)
## R6 2.4.1 2019-11-12 [2] CRAN (R 3.6.0)
## Rcpp 1.0.3 2019-11-08 [2] CRAN (R 3.6.0)
## readr * 1.3.1 2018-12-21 [2] CRAN (R 3.6.0)
## readxl 1.3.1 2019-03-13 [2] CRAN (R 3.6.0)
## remotes 2.1.0 2019-06-24 [2] CRAN (R 3.6.0)
## reprex 0.3.0 2019-05-16 [2] CRAN (R 3.6.0)
## rgeos * 0.5-2 2019-10-03 [2] CRAN (R 3.6.0)
## rlang 0.4.2 2019-11-23 [2] CRAN (R 3.6.1)
## rmarkdown 2.0 2019-12-12 [1] CRAN (R 3.6.0)
## rprojroot 1.3-2 2018-01-03 [2] CRAN (R 3.6.0)
## rstudioapi 0.10 2019-03-19 [2] CRAN (R 3.6.0)
## Rttf2pt1 1.3.7 2018-06-29 [2] CRAN (R 3.6.0)
## rvest 0.3.5 2019-11-08 [2] CRAN (R 3.6.0)
## scales * 1.1.0 2019-11-18 [2] CRAN (R 3.6.0)
## sessioninfo 1.1.1 2018-11-05 [2] CRAN (R 3.6.0)
## sf * 0.8-0 2019-09-17 [2] CRAN (R 3.6.0)
## sp * 1.3-2 2019-11-07 [2] CRAN (R 3.6.0)
## stringi 1.4.3 2019-03-12 [2] CRAN (R 3.6.0)
## stringr * 1.4.0 2019-02-10 [2] CRAN (R 3.6.0)
## testthat 2.3.1 2019-12-01 [2] CRAN (R 3.6.0)
## tibble * 2.1.3 2019-06-06 [2] CRAN (R 3.6.0)
## tidyr * 1.0.0 2019-09-11 [2] CRAN (R 3.6.0)
## tidyselect 0.2.5 2018-10-11 [2] CRAN (R 3.6.0)
## tidyverse * 1.3.0 2019-11-21 [2] CRAN (R 3.6.0)
## transformr 0.1.1 2018-12-09 [2] CRAN (R 3.6.0)
## tweenr 1.0.1 2018-12-14 [2] CRAN (R 3.6.0)
## units 0.6-5 2019-10-08 [2] CRAN (R 3.6.0)
## usethis 1.5.1 2019-07-04 [2] CRAN (R 3.6.0)
## vctrs 0.2.1 2019-12-17 [2] CRAN (R 3.6.0)
## vegan * 2.5-6 2019-09-01 [2] CRAN (R 3.6.0)
## viridis * 0.5.1 2018-03-29 [2] CRAN (R 3.6.0)
## viridisLite * 0.3.0 2018-02-01 [2] CRAN (R 3.6.0)
## withr 2.1.2 2018-03-15 [2] CRAN (R 3.6.0)
## xfun 0.11 2019-11-12 [2] CRAN (R 3.6.0)
## xml2 1.2.2 2019-08-09 [2] CRAN (R 3.6.0)
## yaml 2.2.0 2018-07-25 [2] CRAN (R 3.6.0)
## zeallot 0.1.0 2018-01-28 [2] CRAN (R 3.6.0)
##
## [1] /Users/oliviergimenez/Library/R/3.6/library
## [2] /Library/Frameworks/R.framework/Versions/3.6/Resources/library